UAV path planning algorithm based on improved RRT-Connect

ZHANG Shun; XIE Xihua; CHEN Dingping

doi:10.13873/J.1000-9787(2020)12-0146-03

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UAV path planning algorithm based on improved RRT-Connect

更新时间：2025-09-11

- UAV path planning algorithm based on improved RRT-Connect
- Issue 12, Pages: 146-148+156(2020)
- 作者机构：
  
  1. 中南大学机电工程学院高性能复杂制造国家重点实验室
  2. 湖南山河科技股份有限公司
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2020)12-0146-03
  CLC： V279
- Published：2020
- 稿件说明：
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ZHANG Shun, XIE Xihua, CHEN Dingping. UAV path planning algorithm based on improved RRT-Connect[J]. 2020, (12): 146-148+156.
DOI：

ZHANG Shun, XIE Xihua, CHEN Dingping. UAV path planning algorithm based on improved RRT-Connect[J]. 2020, (12): 146-148+156. DOI： 10.13873/J.1000-9787(2020)12-0146-03.

摘要

针对双向快速扩展随机树(RRT-Connect)在复杂环境中路径规划的采样具有随机性问题

提出一种人工势场法与RRT-Connect算法结合的改进算法(PRRT-Connect)

优化采样和扩展策略;同时采用选择性启发因子选择改进算法搜索树新节点产生的方向;最后根据无人机的性能约束对规划出的路径进行修剪以及采用三阶贝塞尔曲线对最终的路径进行路径平滑

使所规划出的路径更接近最优解。仿真结果表明:改进算法具有效率高、收敛性好、规划能力强和稳定性好的特点

并且能很好地规避突发障碍物

可以很好地解决复杂环境下无人机的路径规划问题。

Abstract

Aiming at randomness problem that the path planning sampling of the two-way fast-expanding random tree（ RRT-Connect） has in the complex static environment

an improved algorithm combining artificial potential field method and RRT-Connect algorithm（ PRRT-Connect） is proposed to optimize sampling and scaling strategies.The selective heuristic factor is used to search the tree row expansion of the RRT-Connect algorithm. The planned path is trimmed according to the performance constraints of the UAV and the final path is performed using the third-order Bezier curve

so that the path planned by the improved algorithm is more close to the optimal path. The research results show that the improved algorithm has the characteristics of high efficiency

good convergence performance and strong planning ability

and it can avoid unexpected obstacles

which can well solve the path planning problem of UAVs in complex environments.

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